Browsing by Author "Vijayalakshmi, M."

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    Advanced thermal vision techniques for enhanced fault diagnosis in electrical equipment: a review
    (Springer, 2025) Anbalagan, A.; Persiya, J.; Mohamed Mansoor Roomi, S.; Arumuga Perumal, D.A.; Poornachari, P.; Vijayalakshmi, M.; Ebenezer, L.
    Ensuring the reliability and safety of electrical equipment is essential for industrial and residential applications. Traditional fault diagnosis methods involving physical inspections are time-consuming and ineffective for early fault detection. Infrared (IR) thermography offers a non-invasive and efficient solution by identifying anomalies in temperature profiles. This review explores thermal vision-based fault diagnosis techniques, including region of interest (ROI) segmentation, image pre-processing, and fault diagnosis algorithms, with a focus on deep learning approaches. The study highlights the effectiveness of machine learning models in enhancing fault detection accuracy while identifying challenges such as environmental variations, data inconsistencies, and system integration issues. The review discusses the role of real-time applications, wireless technologies, and AI-based automation in improving fault detection. Research gaps are identified, and future directions are proposed to enhance efficiency, reliability, and industrial adoption. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025.
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    AntLion Optimization Algorithm based Type II Diabetes Mellitus Prediction
    (Institute of Electrical and Electronics Engineers Inc., 2022) Anbalagan, A.; Baskar, C.; Deekshetha, H.R.; Reshma, S.; Vijayalakshmi, M.; Arumuga Perumal, D.
    Diabetes Mellitus is one of the common diseases prevailing in most developed and developing countries. In recent decades, there has been a huge rise in diabetes patients in India. Based on recent statistics, nearly 72.96 million young people are suffering from diabetes. Thus, it is essential to diagnose diabetes at an early stage. In this work, the PIMA dataset is used to design an optimized and super-vised learning model based on K-nearest neighbor classification. The optimization algorithm used to generate useful features to predict diabetes mellitus is the Antlion optimization algorithm. The proposed work yields an accuracy of 80% for the selected features like Pregnancy, BMI, BP, Age, Glucose, and Diabetes Pedigree Function. © 2022 IEEE.